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87 lines
3.5 KiB
87 lines
3.5 KiB
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2017 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/invert_psd_matrix.h"
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#include "ceres/internal/eigen.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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static const bool kFullRank = true;
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static const bool kRankDeficient = false;
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template <int kSize>
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typename EigenTypes<kSize, kSize>::Matrix RandomPSDMatrixWithEigenValues(
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const typename EigenTypes<kSize>::Vector& eigenvalues) {
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typename EigenTypes<kSize, kSize>::Matrix m;
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m.setRandom();
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Eigen::SelfAdjointEigenSolver<typename EigenTypes<kSize, kSize>::Matrix> es(
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m);
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return es.eigenvectors() * eigenvalues.asDiagonal() *
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es.eigenvectors().transpose();
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}
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TEST(InvertPSDMatrix, Identity3x3) {
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const Matrix m = Matrix::Identity(3, 3);
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const Matrix inverse_m = InvertPSDMatrix<3>(kFullRank, m);
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EXPECT_NEAR((inverse_m - m).norm() / m.norm(),
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0.0,
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std::numeric_limits<double>::epsilon());
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}
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TEST(InvertPSDMatrix, FullRank5x5) {
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EigenTypes<5>::Vector eigenvalues;
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eigenvalues.setRandom();
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eigenvalues = eigenvalues.array().abs().matrix();
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const Matrix m = RandomPSDMatrixWithEigenValues<5>(eigenvalues);
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const Matrix inverse_m = InvertPSDMatrix<5>(kFullRank, m);
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EXPECT_NEAR((m * inverse_m - Matrix::Identity(5,5)).norm() / 5.0, 0.0,
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std::numeric_limits<double>::epsilon());
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}
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TEST(InvertPSDMatrix, RankDeficient5x5) {
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EigenTypes<5>::Vector eigenvalues;
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eigenvalues.setRandom();
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eigenvalues = eigenvalues.array().abs().matrix();
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eigenvalues(3) = 0.0;
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const Matrix m = RandomPSDMatrixWithEigenValues<5>(eigenvalues);
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const Matrix inverse_m = InvertPSDMatrix<5>(kRankDeficient, m);
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Matrix pseudo_identity = Matrix::Identity(5, 5);
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pseudo_identity(3, 3) = 0.0;
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EXPECT_NEAR((m * inverse_m * m - m).norm() / m.norm(),
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0.0,
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10 * std::numeric_limits<double>::epsilon());
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}
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} // namespace internal
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} // namespace ceres
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